图像拼接技术的入门术语

Homography(透视变换)

A Homography is a transformation ( a 3×3 matrix ) that maps the points in one image to the corresponding points in the other image.

就是两幅图的对应点的变换关系（是一个矩阵），对应点以上图，四个不同颜色点为例。

如

以红色点为例（x1,y1）<-->（x2,y2），有如下变换关系

利用单应性进行图像匹配（配准）

全景：透视应用

       在前一节中，我们知道，如果两个图像之间的单应性已知，我们可以将一个图像扭曲到另一个图像上。然而，有一个大的警告。图像必须包含一个平面（一本书的顶部），只有平面部分被正确对齐。事实证明，如果你拍第一张照片，任何场景（不只是一个平面），然后通过旋转相机拍了第二图像，两图像将被联系通过单应性。

计算透视变换矩阵

      为了计算两幅图像之间的单应性，需要知道两幅图像之间至少有4点对应。如果你有4个以上的对应点，那就更好了。OpenCV将稳健估计单应性，最适合所有对应点。通常，这些点对应是通过匹配图像之间的SIFT或SURF等特征自动发现的。

C++

// pts_src and pts_dst are vectors of points in source
// and destination images. They are of type vector<Point2f>.
// We need at least 4 corresponding points.
Mat h = findHomography(pts_src, pts_dst);
// The calculated homography can be used to warp
// the source image to destination. im_src and im_dst are
// of type Mat. Size is the size (width,height) of im_dst.
warpPerspective(im_src, im_dst, h, size);

Python

'''
pts_src and pts_dst are numpy arrays of points
in source and destination images. We need at least
4 corresponding points.
'''
h, status = cv2.findHomography(pts_src, pts_dst)
'''
The calculated homography can be used to warp
the source image to destination. Size is the
size (width,height) of im_dst
'''
im_dst = cv2.warpPerspective(im_src, h, size)

OpenCV的C++单应性实例

在图2中的图像。可以使用下面的C++代码生成。下面的代码显示了如何在两幅图像中获取四个对应点，并将扭曲图像带到另一个图像上。

#include "opencv2/opencv.hpp" 
 
using namespace cv;
using namespace std;
 
int main( int argc, char** argv)
{
    // Read source image.
    Mat im_src = imread("book2.jpg");
    // Four corners of the book in source image
    vector<Point2f> pts_src;
    pts_src.push_back(Point2f(141, 131));
    pts_src.push_back(Point2f(480, 159));
    pts_src.push_back(Point2f(493, 630));
    pts_src.push_back(Point2f(64, 601));
 
 
    // Read destination image.
    Mat im_dst = imread("book1.jpg");
    // Four corners of the book in destination image.
    vector<Point2f> pts_dst;
    pts_dst.push_back(Point2f(318, 256));
    pts_dst.push_back(Point2f(534, 372));
    pts_dst.push_back(Point2f(316, 670));
    pts_dst.push_back(Point2f(73, 473));
 
    // Calculate Homography
    Mat h = findHomography(pts_src, pts_dst);
 
    // Output image
    Mat im_out;
    // Warp source image to destination based on homography
    warpPerspective(im_src, im_out, h, im_dst.size());
 
    // Display images
    imshow("Source Image", im_src);
    imshow("Destination Image", im_dst);
    imshow("Warped Source Image", im_out);
 
    waitKey(0);
}

 python

#!/usr/bin/env python
 
import cv2
import numpy as np
 
if __name__ == '__main__' :
 
    # Read source image.
    im_src = cv2.imread('book2.jpg')
    # Four corners of the book in source image
    pts_src = np.array([[141, 131], [480, 159], [493, 630],[64, 601]])
 
 
    # Read destination image.
    im_dst = cv2.imread('book1.jpg')
    # Four corners of the book in destination image.
    pts_dst = np.array([[318, 256],[534, 372],[316, 670],[73, 473]])
 
    # Calculate Homography
    h, status = cv2.findHomography(pts_src, pts_dst)
     
    # Warp source image to destination based on homography
    im_out = cv2.warpPerspective(im_src, h, (im_dst.shape[1],im_dst.shape[0]))
     
    # Display images
    cv2.imshow("Source Image", im_src)
    cv2.imshow("Destination Image", im_dst)
    cv2.imshow("Warped Source Image", im_out)
 
    cv2.waitKey(0)